Control Room of the Future
Sia Partners x NVIDIA
Becoming an NVIDIA Solution Advisor to accelerate AI Transformation for Our Clients
01 Highlights of our partnership
Leveraging our deep expertise in AI and data, we have cultivated a strong collaboration with NVIDIA. This enables us to support our clients in implementing and accelerating their AI journeys using NVIDIA’s cutting-edge technology and solutions, particularly in fields such as computer vision and Generative AI (GenAI), with plans to expand into many more areas in the near future.
Through this collaboration, we empower our clients to scale their AI initiatives faster and more efficiently. By integrating NVIDIA’s industry-leading technology, we can deliver custom-built models that are precisely tailored to their needs, helping them achieve their goals more effectively.
Our AI Centers of Excellence located across Europe, North America, Benelux, and Australia will receive advanced training on NVIDIA's AI platforms, including NVIDIANIM™ (inference model), NVIDIA NeMo™ (platform for large language models), and NVIDIA Metropolis (intelligent video analytics platform).
Initially, our collaboration with NVIDIA will focus on delivering AI-powered solutions that accelerate time-to-value in key industries such as energy and climate transition, retail and luxury, banking and insurance, as well as manufacturing, media, and telecommunications.
Our combined expertise in Data and AI, coupled with our broad sectoral and functional knowledge, uniquely positions us as transformational advisors. We help clients not only to identify and solve their most complex challenges but also to foster sustainability and competitive advantage in a rapidly evolving marketplace.
By partnering with us, our clients gain access to a dedicated team of seasoned professionals who are fully committed to their success. We work closely to understand their business objectives and create tailored solutions that deliver measurable, impactful results, ensuring they stay ahead in the dynamic world of AI and data innovation.
02 Our offering for Retail & CPG
Retail & Consumer Packaged Goods (CPG) actors have identified AI as critical to their future success.
Key Challenges for Retailers.
Consumer behavior is rapidly shifting, making it critical for retailers to transform their business to respond to market demand and opportunity. Retailers need real-time speed, predictability, and accuracy.
Key Business Drivers for AI Adoption.
- Meeting consumer expectations: Retailers need to cater to faster, more personalized shopping experiences.
- Operational efficiency: AI is helping retailers streamline processes like forecasting, inventory management, and distribution.
- Cost reduction: Automation through AI reduces labor challenges and operational costs, especially in distribution centers.
AI Adoption and Profitability in Retail.
40% of retailers have already adopted AI, with projections showing this will reach 80% in the next three years.
Retail is expected to generate $1.7 trillion annually from AI and analytics, showing how deeply AI will drive profitability.
NVIDIA Solutions address the most important AI use cases in Retail & CPG
AI-powered intelligent stores help retailers reduce shrinkage, prevent stock outs, and gain insights into customer behavior. Using AI and computer vision, they can analyze camera and sensor data to make real-time decisions, improve operations, and boost efficiency.
- Customer and Store Analytics: Provide actionable insights into customer behavior and store performance.
- Store Simulation: Optimize store layout and merchandising for better customer flow and product placement.
- Autonomous Shopping: Enable a checkout-free experience for seamless shopping.
- Stock out Management: Automate tasks to reduce stock outs and assist retail associates.
- Asset Protection: Prevent shrinkage at the point of sale through advanced monitoring and security systems.
Create a seamless experience across stores, web, and mobile, as 73% of customers use multiple channels during a single shopping journey. By leveraging AI and generative AI, retailers can enhance the omnichannel experience, boosting cart size and brand loyalty.
- Personalized Recommendations: Boost sales and customer loyalty.
- Content creation: Quickly create new content with Generative AI.
- Shopping Assistants: Enhance customer engagement with conversational AI.
- Auto-tagging: Improve search and recommendations.
- Cybersecurity: Detect threats faster.
AI and simulation solutions boost supply chain efficiency, helping retailers meet customer expectations. With intelligent video analytics, robotics, and automation, operations run smoother, throughput increases, and warehouse robots ensure accurate order fulfillment.
- Warehouse Simulation: Optimizing the distribution center.
- Warehouse Analytics: Improving efficiency and accuracy.
- Demand forecasting: Increase forecast accuracy.
- Package handling and route optimization: Boosting warehouse efficiency.
- Last-Mile Delivery: Enhancing delivery routes.
A selection of NVIDIA Solutions we leverage to accelerate our work
NVIDIA Metropolis is an AI-enabled video analytics platform that helps retailers harness the power of real-time data from cameras and sensors.
Use cases:
- Loss prevention and theft detection
- Foot traffic analysis
- Queue management
- In-Store marketing effectiveness
How it accelerates our work:
Accelerated Delivery with a Ready-Made Platform:
- Metropolis provides a ready-made platform for video analytics and real-time data processing.
Pre-Built Video Analytics Models:
- Includes pre-built models for surveillance and object detection, which saves our data team significant time on development.
Customizable Solutions:
- Allows focus on customizing solutions such as in-store analytics dashboards, Customer behavior tracking and Staff optimization based on foot traffic.
NVIDIA Omniverse is an end-to-end platform that enables retailers to build and operate digital twins of physical spaces like stores, warehouses, and distribution centers. This allows for real-time collaboration, simulation, and optimization of operations in a virtual environment.
Use cases:
- Store layout optimization
- Supply chain and Inventory simulation
- Customer journey mapping
How it accelerates our work:
Accelerated Digital Twin Development:
- Omniverse offers a fully integrated platform for building and operating digital twins.
Simulating Retail Environments:
- Enables simulation of retail environments without physical testing and drastically reduces time and resources required for planning and iteration.
Quick Simulations:
- Facilitates rapid simulations of store layouts, supply chains, customer journeys.
- Eliminates the need to patch together multiple third-party systems.
NVIDIA Riva is a platform for building and deploying conversational AI applications using deep learning models for speech recognition, language understanding, and text-to-speech.
Use cases:
- Multilingual customer service chatbots
- Personalized virtual shopping assistants
- Self-Service for orders and returns
- Employee support systems (virtual assistants)
How it accelerates our work:
Accelerated Conversational AI Deployment:
- NVIDIA Riva offers pre-trained models for Speech recognition, Language understanding, Speech synthesis.
Reduced Development Time:
- Significantly cuts down development time.
AI-powered Customer Service Solutions:
- Enables rapid delivery of Chatbots and Virtual assistants.
03 Our offering for Financial Services
AI Adoption in Financial Services.
90%+ of financial services institutions plan to increase AI spending.
AI revolutionizes processes across:
- Banking
- Asset management
- Insurance
- Fintech
AI's Impact on Fraud Detection & Efficiency.
- AI-driven systems reduce false positives by 40% in fraud detection, improving detection rates by up to 6%
- 35% of companies report improved workflows with AI
- 20%+ of firms have seen cost reductions from AI-driven efficiencies
of financial institutions reduced costs by 10%+ thanks to AI.
36%
The most important AI use cases in Financial Services are addressed by NVIDIA Solutions
NVIDIA’s AI solutions can be used to combat fraud and enhance cybersecurity by providing financial institutions with real-time data analysis and predictive capabilities. NVIDIA’s AI models significantly reduce false positives and improve the accuracy of fraud detection systems.
- Transaction fraud detection: Analyze vast datasets to detect fraudulent transactions in real-time with reduced false positives.
- Anti-Money Laundering (AML) compliance: Use AI to track and analyze transactions, detecting suspicious patterns to enhance compliance.
- Know Your Customer (KYC): Verify customer identity with AI models that cross-check multiple data sources.
- Phishing and malware detection: Leverage NLP and machine learning to identify phishing emails and malicious communications.
- Cyber threat detection: Monitor and analyze network activity for unusual patterns, identifying potential cyber threats.
AI enhances customer interactions through personalized services and automation. Solutions like conversational AI and recommendation engines streamline customer service, allowing for faster, more efficient responses.
- Virtual assistants (Conversational AI): Automate customer service tasks, such as account inquiries and transaction processing.
- Recommendation systems: Offer personalized product suggestions based on customer behavior and financial history.
- Sentiment analysis: Analyze customer feedback to improve service quality and tailor products.
- Speech AI and multilingual support: Provide real-time, multi-language support, enhancing the customer experience in diverse markets.
AI optimizes risk management and trading strategies by analyzing large datasets in real-time. This enables financial institutions to make more informed decisions regarding investments and market risks.
- Compliance monitoring: Ensure compliance with regulatory standards by continuously monitoring transactions and market behaviors.
- Risk analytics: Assess and predict financial risks by analyzing market data in real-time.
- Algorithmic trading: Automate trading strategies using AI to execute trades faster and more efficiently.
- Portfolio optimization: Adjust portfolios dynamically, based on market conditions and client preferences using AI-driven models.
A selection of NVIDIA Solutions we leverage to accelerate our work
Triton is an open-source inference-serving software that enables model deployment at scale. It supports various deep learning frameworks and can serve models for real-time or batch inference.
Use Cases:
- Real-time fraud detection
- Anomaly detection in financial transactions
- KYC compliance checks
How it accelerates our work
Rapid AI Model Deployment:
- Triton enables fast deployment of AI models across various environments.
- Supports on-premises or cloud-based implementations.
- Enhances fraud detection by reducing latency in applications.
Flexible & Scalable Solution:
- Offers financial institutions a flexible and scalable infrastructure.
- Adapts to high-frequency transaction handling.
Maxine is an AI-powered platform for video and audio communications, including real-time transcription, translation, and video enhancements.
Use Cases:
- Virtual financial advisors
- AI-enhanced video conferencing for customer support
- Fraud prevention via voice biometrics
How it accelerates our work
Rapid AI Tool Deployment:
- Maxine allows our data team to quickly deploy AI-powered communication tools.
- Examples include virtual financial assistants and voice-enabled security systems.
Pre-Trained Models for Real-Time Interactions:
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Maxine provides pre-trained models optimized for real-time customer interactions.
Merlin is a deep learning framework designed to build and deploy high-performance recommender systems.
Use Cases:
- Personalized financial product recommendations
- Personalized customer experience
- Investment advice suggestions
How it accelerates our work
Accelerated Development of Recommendation Systems:
- Merlin speeds up the creation of recommendation systems for financial clients.
- Enables hyper-personalized services for customers.
GPU-Accelerated Framework:
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Merlin's GPU acceleration reduces model training time.
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Crucial for clients working with massive datasets.
RAPIDS is a suite of open-source libraries that accelerate data science and machine learning pipelines using GPUs.
Use Cases:
- Fraud detection
- Real-time data processing
- Anti-money laundering (AML)
How it accelerates our work
Faster Model Training & Data Processing:
- RAPIDS significantly reduces time spent on model training and data processing.
- Optimized for fraud detection and AML (Anti-Money Laundering) use cases.
GPU-Accelerated Data Analytics:
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Leverages GPU acceleration to deliver faster insights into fraudulent activity or suspicious transactions.
04 Our offering for Energy & Utilities
Why AI is Essential in the Energy & Utilities Industry
Artificial Intelligence is transforming the energy and utilities industry, enabling greater efficiency, optimizing grid management, and driving the shift toward renewable energy through advanced, data-driven technologies.
Key Insights
- The use of AI in the energy market is expected to grow at a CAGR of 30.1% from 2024 to 2030.
- 89% of energy executives believe AI will be a key differentiator in the energy transition.
- AI-driven optimization in operations can result in up to 40% reduction in expenditure.
The most important AI use cases for Energy & Utilities are addressed by NVIDIA Solutions
AI-driven models predict energy demand, manage decentralized resources, and simulate grid performance to minimize downtime and maximize efficiency.
- Industrial Digital Twins: Using digital twins, utilities providers can predict needs for power plants, optimize energy generation, and prevent downtime.
- Power Grid Simulation: AI models simulate real-time grid operations, enabling predictive maintenance and outage management. This helps utilities players respond to disruptions more efficiently.
- Smart Metering and Grid Edge Management: AI models can predict the load on the grid and manage distributed energy resources (DERs) like home batteries and solar panels. Real-time data analytics from smart meters helps optimize grid performance and prevent blackouts.
AI accelerates the inspection and management of physical assets, automating tasks like inspection and leak detection. Autonomous operations reduce the need for on-site personnel, enhancing safety and reducing operational costs.
- Automated Asset Inspection: AI-enabled systems analyze imagery of power lines and poles, identifying potential hazards (vegetation overgrowth, equipment damage...). These inspections prevent power outages and wildfire risks.
- Predictive Maintenance: AI models predict when critical equipment will fail, allowing utilities providers to replace parts before they break down.
- Autonomous Operations in Power Plants: AI-powered edge devices monitor power plant safety and efficiency. Autonomous systems detect leaks and hazards in real time, enabling faster response and reducing human intervention.
AI enhances customer service for utilities actors by improving call center performance, forecasting energy usage, and providing personalized insights for customers.
- Call Center AI Assistants: Virtual assistants handle customer queries, provide outage updates… These AI systems improve customer satisfaction and reduce call resolution times.
- Smart Homes and Energy Hubs: AI-based systems predict energy demand for smart homes (solar panels, electric vehicles…). This helps customers reduce energy costs and manage their consumption more efficiently.
- Energy Usage Forecasting and Optimization: AI models analyze data to forecast energy demand for individual households or businesses to offer personalized energy-saving recommendations and optimize load management.
A selection of NVIDIA Solutions we leverage to accelerate our work
Triton is an open-source inference serving software that enables model deployment at scale. It supports various deep learning frameworks and can serve models for real-time or batch inference.
Use Cases:
- Autonomous operations in power plants
- Real-time monitoring of energy infrastructure
- Predictive maintenance
How it accelerates our work
Superior Scalability
- Triton offers superior scalability compared to other platforms.
- Enables real-time AI model deployment for utilities.
Support for Multiple AI Frameworks
- Facilitates the automation of power plants and grid management.
- Streamlines operations by supporting multiple AI frameworks.
NVIDIA Omniverse is an end-to-end platform that enables utilities players to build and operate digital twins of physical spaces like plants and warehouses. This allows for real-time collaboration, simulation, and optimization of operations in a virtual environment.
Use Cases:
- Power grid optimization
- Digital twins for energy assets
- Maintenance prediction and grid simulations
How it accelerates our work
Accelerated Digital Twin Development:
- Omniverse provides a fully integrated platform for building and operating digital twins.
- Enables simulation of utilities environments without physical testing.
- Reduces time and resources required for planning and iteration.
Superior Interoperability:
- Offers better interoperability with existing 3D tools compared to other market options and includes built-in AI capabilities for seamless simulations.
Efficient Simulations:
- Enables simulations for grid optimization, predictive maintenance, and more.
NVIDIA Riva is a platform for building and deploying conversational AI applications using deep learning models for speech recognition, language understanding, and text-to-speech.
Use Cases:
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Virtual call centers
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Voice-enabled plant operations
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Smart assistant for utilities
How it accelerates our work
Accelerated Conversational AI Deployment:
- NVIDIA Riva speeds up the deployment of conversational AI solutions.
- Provides pre-trained models for speech recognition, language understanding, and speech synthesis.
Reduced Development Time:
- Significantly cuts down the time needed for development.
05 Our offering for BioPharma & Pharmaceuticals
Challenges in AI-Powered Drug Discovery: Speed, Cost, and Success Rates
Enhancing the speed and quality of early preclinical drug discovery is crucial for unlocking life-saving therapies. Traditional methods are slow and costly, averaging 10 years and $2 billion per new drug, with only 10% success. As the pharma and biopharma industries turn to computer-aided techniques like molecular modeling and virtual screening, AI is emerging as a vital tool to accelerate and improve drug discovery.
To unlock the full potential of AI for drug discovery, the industry needs:
- A domain-specific platform for developing, customizing, and deploying biomolecular AI models at scale.
- Scalable, distributed GPU computing for training generative AI without the overhead of setting up and maintaining the necessary infrastructure.
- Deployment of GenAI into production, where optimized runtimes and autoscaling can generate, predict, and screen the near-infinite number of potential drug-target combinations.
Key challenges for AI drug discovery.
- Scaling: Advancing from proof of concept to enterprise deployment requires effective scaling through the efficient use of resources to ensure manageability, availability, and infrastructure cost.
- Performance: High performance with multi-node, multi-GPU support is critical for AI.
- Application Development and Deployment: Pulling together disparate products into an end-to-end AI solution for drug discovery is difficult.
Benefits for AI drug discovery researchers and developers.
- Accelerated AI training, customization, and deployment.
- Streamlined path for developing and deploying AI-powered drug discovery applications.
- Pretrained state-of-the-art biomolecular models.
NVIDIA AI Use Cases Revolutionizing Drug Discovery
Proteins are essential molecules, and their structure is directly linked to their function. Misfolded proteins are often implicated in degenerative diseases, such as Alzheimer’s and Parkinson’s.
Predicting how proteins fold into their functional 3D structures is crucial for understanding these diseases and for designing new drugs.
Challenges:
- Complexity: Protein folding is highly complex, requiring significant computational power.
- Accuracy: Traditional methods are slow and resource-intensive, limiting early-stage drug discovery.
Exploring the chemical space to identify drug candidates is one of the critical steps in drug discovery.
AI-driven generative chemistry models allow researchers to design new small molecules from scratch (de novo), dramatically speeding up the process of discovering new drug candidates.
Challenges:
- Molecule viability: Designing molecules that are chemically stable, bioavailable, and effective against the target protein.
- Chemical space exploration: The chemical space is extremely large, making manual or traditional computational searches inefficient.
Predicting the physical, chemical, and biological properties of molecules is crucial for evaluating the viability of drug candidates.
By understanding them early on, researchers can eliminate unsuitable compounds before advancing into testing, saving time and resources.
Challenges:
- Data complexity: Predictions rely on vast, complex datasets that are computationally demanding.
- Biological relevance: Translating chemical properties into biological effects is complex and difficult to model accurately.
The biomedical field generates large amounts of unstructured data from research papers and clinical trials.
Biomedical NLP allows researchers to automatically extract relevant insights, helping them answer questions related to drug discovery, clinical trials, and patient outcomes.
Challenges:
- Unstructured text: Biomedical data is hard to process.
- Data overload: The vast amount of biomedical literature makes manual analysis impractical.
- Domain specificity: NLP models must be trained on specialized biomedical terms and concepts.
A selection of NVIDIA Solutions we leverage to accelerate our work
NVIDIA BioNeMo™ simplifies and accelerates AI model development and deployment for drug discovery, covering the entire pipeline from target identification to lead optimization. It offers AI workflows for 3D protein structure prediction, small molecule design, virtual screening, and property prediction, along with pre-trained models and optimized scaling recipes.
How it accelerates our work:
- BioNeMo reduces the time spent on model development, providing ready-to-use workflows for key drug discovery tasks.
- This allows our Data teams to focus on the final 20% of customization, integrating client data, refining outputs, and optimizing workflows, drastically shortening project timelines and enhancing delivery speed.
NVIDIA Clara Discovery is an AI and high-performance computing (HPC) platform for accelerating drug discovery, particularly in areas like computational chemistry, molecular simulations, and imaging.
How it accelerates our work:
- This platform's pre-built pipelines and high-speed computational capabilities allow us to skip manual, time-consuming processes, focusing instead on customizing and optimizing the results to meet specific client requirements.
- This allows us to deliver solutions faster, with reduced engineering time, while ensuring accuracy and scalability.
NVIDIA NIM is a set of cloud-native microservices for deploying AI models across drug discovery applications. It includes services like MolMIM for generative chemistry, ESMFold for protein prediction, and DiffDock for molecular interactions, available via APIs or self-hosted models for flexible deployment.
How it accelerates our work:
- NIM’s microservices enable quick deployment of AI models across various environments, reducing complexity and setup time.
- This lets us fast-track drug discovery solutions, focusing on customizing and optimizing workflows for client-specific needs, ensuring faster delivery and reduced costs.
